Systematic identification of latent disease-gene associations from PubMed articles

Yuji Zhang, Feichen Shen, Majid Rastegar Mojarad, Dingcheng Li, Sijia Liu, Cui Tao, Yue Yu, Hongfang Liu

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

Original languageEnglish (US)
Article numbere0191568
JournalPloS one
Issue number1
StatePublished - Jan 2018

ASJC Scopus subject areas

  • General


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